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1
Understanding Tonal Languages
In: DTIC (2013)
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2
Machine Recognition vs Human Recognition of Voices
In: DTIC (2012)
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3
Iterated Class-Specific Subspaces for Speaker-Dependent Phoneme Classification
In: DTIC (2008)
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4
Multimodal Meeting Capture and Understanding with the CALO Meeting Assistant
In: DTIC (2007)
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5
Error Awareness and Recovery in Conversational Spoken Language Interfaces
In: DTIC (2007)
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6
Using Prosody for Automatic Sentence Segmentation of Multi-Party Meetings
In: DTIC (2006)
Abstract: We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data. We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter). We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR). We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features. Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement. ; Presented at the International Conference on Text, Speech, and Dialogue (9th) (TSD 2006) held in Brno, Czech Republic on 11-15 Sep 2006. Published in the Proceedings of the International Conference on Text, Speech, and Dialogue (9th), 2006. Sponsored in part by National Science Foundation contract no. IIS-0121396.
Keyword: *BOUNDARIES; *MODELS; *PROSODIC FEATURES; *PROSODY; *SENTENCE SEGMENTATION; *SPEECH RECOGNITION; ALGORITHMS; ASR(AUTOMATIC SPEECH RECOGNIZER); AUTOMATIC; BOOSTING; CLASSIFICATION; CLASSIFIERS; Cybernetics; HMM(HIDDEN MARKOV MODELS); LEXICAL FEATURES; Linguistics; MARKOV PROCESSES; PAUSES; SYMPOSIA; TEAMS(PERSONNEL); Voice Communications; WORDS(LANGUAGE)
URL: http://www.dtic.mil/docs/citations/ADA459015
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA459015
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7
Investigation on Mandarin Broadcast News Speech Recognition
In: DTIC (2006)
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8
Thai Automatic Speech Recognition
In: DTIC (2005)
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9
Towards a Warfighter's Associate: Eliminating the Operator Control Unit
In: DTIC (2004)
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10
Natural Language Generation in Dialog Systems
In: DTIC (2001)
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11
Isolated Speech Recognition Using Artificial Neural Networks
In: DTIC (2001)
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12
Improving Acoustic Models by Watching Television
In: DTIC AND NTIS (1998)
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13
Co-Channel Speech and Speaker Identification Study
In: DTIC AND NTIS (1998)
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14
Implementing Voice Recognition and Natural Language Processing in the NPSNET Networked Virtual Environment.
In: DTIC AND NTIS (1996)
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15
Automatic Language Identification with Sequences of Language-Independent Phoneme Clusters.
In: DTIC AND NTIS (1996)
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16
Technology Review of the Use of Continuous Speech Recognition for Language Training.
In: DTIC AND NTIS (1996)
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17
Knowledge Representation in the TRAINs-93 Conversation System.
In: DTIC AND NTIS (1996)
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18
Diphone-Based Speech Recognition Using Neural Networks.
In: DTIC AND NTIS (1996)
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19
A Robust Loose Coupling for Speech Recognition and Natural Understanding.
In: DTIC AND NTIS (1995)
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20
Spoken Dialogue Understanding and Local Context.
In: DTIC AND NTIS (1994)
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